Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add commands/orq-ai/assistant-plugins/analyticsgit clone --depth 1 https://github.com/orq-ai/assistant-pluginsWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00021 | $0.01230 |
| Opus 5 | $0.00010 | $0.00615 |
| Sonnet 5 | $0.00004 | $0.00246 |
| Haiku 4.5 | $0.00002 | $0.00123 |
Grade A, and why
analytics scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 110 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Analytics
Show workspace analytics from orq.ai — request volume, cost, token usage, error rates, and top models. Use this to get a quick health check or drill into usage trends.
Instructions
1. Parse arguments
Extract options from $ARGUMENTS. All are optional:
--last <duration>— time window:1h,6h,24h,7d,30d(default:24h)--group-by <dimension>— drill-down dimension:model,deployment,status,agent(optional)
If $ARGUMENTS is empty, show the overview for the last 24 hours.
If the user provides plain text instead of flags (e.g., /orq:analytics cost by model last 7 days), interpret the intent and map to the appropriate options.
2. Fetch analytics data
Run these MCP calls in parallel:
- Overview: Use
get_analytics_overviewto fetch the high-level summary (requests, cost, tokens, errors). - Drill-down (if
--group-byprovided): Usequery_analyticswith the specified dimension to fetch grouped breakdowns.
If no --group-by is specified, still call query_analytics grouped by model to show the top models breakdown — this is almost always useful context.
3. Display the overview
Present analytics using native markdown formatting — not inside a code block.
Overview format:
# Orq.ai Analytics — Last 24h
**12,450** requests · **$8.42** cost · **1.2M** tokens · **0.03%** error rate
View detailed analytics at **[Analytics → my.orq.ai](https://my.orq.ai/)**.
---
### Request Volume
- **Total:** 12,450 requests
- **Success:** 12,412 (99.97%)
- **Errors:** 38 (0.03%)
- **Avg latency:** 340ms
- **Avg TTFT:** 120ms
### Cost Breakdown
- **Total:** $8.42
- **Input tokens:** 890,000 ($3.12)
- **Output tokens:** 310,000 ($5.30)
### Top Models
| # | Model | Requests | Cost | Avg Latency |
|---|-------|----------|------|-------------|
| 1 | gpt-4.1 | 5,200 | $4.10 | 420ms |
| 2 | claude-sonnet-4-5 | 3,800 | $3.20 | 380ms |
| 3 | gpt-4.1-mini | 2,100 | $0.85 | 180ms |
| 4 | gemini-2.5-flash | 1,350 | $0.27 | 150ms |
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 110 lines · 21 tokens per session scan A 72d70fa68399
analytics is a command published in the GitHub repository orq-ai/assistant-plugins (6 stars, last pushed 5d ago), licensed MIT. It adds 21 tokens to every session and 1,230 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
Other commands, from other repositories
checklist
Generate a custom checklist for the current feature based on user requirements.
clarify
Identify underspecified areas in the current feature spec by asking up to 5 highly targeted clarification questions and encoding answers back into the spec.
specify
Create or update the feature specification from a natural language feature description.
analyze
Perform a non-destructive cross-artifact consistency and quality analysis across spec.md, plan.md, and tasks.md after task generation.
converge
Assess the current codebase against the feature's spec, plan, and tasks, then append any remaining unbuilt work as new tasks to tasks.md so implement can complete it.
implement
Execute the implementation plan by processing and executing all tasks defined in tasks.md.